Valdés Hernández Maria Del C, Chappell Francesca M, Muñoz Maniega Susana, Dickie David Alexander, Royle Natalie A, Morris Zoe, Anblagan Devasuda, Sakka Eleni, Armitage Paul A, Bastin Mark E, Deary Ian J, Wardlaw Joanna M
Department of Neuroimaging Sciences, Centre for Clinical Brain Sciences, University of Edinburgh, Chancellor's Building, 49 Little France Crescent, Edinburgh, EH16 4SB, UK.
Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK.
Neuroradiology. 2017 Oct;59(10):951-962. doi: 10.1007/s00234-017-1892-1. Epub 2017 Aug 16.
Quantitative assessment of white matter hyperintensities (WMH) on structural Magnetic Resonance Imaging (MRI) is challenging. It is important to harmonise results from different software tools considering not only the volume but also the signal intensity. Here we propose and evaluate a metric of white matter (WM) damage that addresses this need.
We obtained WMH and normal-appearing white matter (NAWM) volumes from brain structural MRI from community dwelling older individuals and stroke patients enrolled in three different studies, using two automatic methods followed by manual editing by two to four observers blind to each other. We calculated the average intensity values on brain structural fluid-attenuation inversion recovery (FLAIR) MRI for the NAWM and WMH. The white matter damage metric is calculated as the proportion of WMH in brain tissue weighted by the relative image contrast of the WMH-to-NAWM. The new metric was evaluated using tissue microstructure parameters and visual ratings of small vessel disease burden and WMH: Fazekas score for WMH burden and Prins scale for WMH change.
The correlation between the WM damage metric and the visual rating scores (Spearman ρ > =0.74, p < 0.0001) was slightly stronger than between the latter and WMH volumes (Spearman ρ > =0.72, p < 0.0001). The repeatability of the WM damage metric was better than WM volume (average median difference between measurements 3.26% (IQR 2.76%) and 5.88% (IQR 5.32%) respectively). The follow-up WM damage was highly related to total Prins score even when adjusted for baseline WM damage (ANCOVA, p < 0.0001), which was not always the case for WMH volume, as total Prins was highly associated with the change in the intense WMH volume (p = 0.0079, increase of 4.42 ml per unit change in total Prins, 95%CI [1.17 7.67]), but not with the change in less-intense, subtle WMH, which determined the volumetric change.
The new metric is practical and simple to calculate. It is robust to variations in image processing methods and scanning protocols, and sensitive to subtle and severe white matter damage.
在结构磁共振成像（MRI）上对白质高信号（WMH）进行定量评估具有挑战性。重要的是，不仅要考虑体积，还要考虑信号强度，使不同软件工具的结果保持一致。在此，我们提出并评估一种满足这一需求的白质（WM）损伤指标。
我们从参与三项不同研究的社区居住老年人和中风患者的脑结构MRI中获取WMH和正常白质（NAWM）体积，采用两种自动方法，随后由两到四名彼此不知情的观察者进行手动编辑。我们计算了NAWM和WMH在脑结构液体衰减反转恢复（FLAIR）MRI上的平均强度值。白质损伤指标的计算方法是，WMH在脑组织中的比例，通过WMH与NAWM的相对图像对比度加权。使用组织微观结构参数以及小血管疾病负担和WMH的视觉评分对新指标进行评估：WMH负担的 Fazekas 评分和WMH变化的 Prins 量表。
WM损伤指标与视觉评分之间的相关性（Spearman ρ >= 0.74，p < 0.0001）略强于视觉评分与WMH体积之间的相关性（Spearman ρ >= 0.72，p < 0.0001）。WM损伤指标的可重复性优于WM体积（测量值之间的平均中位数差异分别为3.26%（四分位间距2.76%）和5.88%（四分位间距5.32%））。即使在对基线WM损伤进行校正后，随访时的WM损伤与总Prins评分仍高度相关（协方差分析，p < 0.0001），而WMH体积并非总是如此，因为总Prins与高强度WMH体积的变化高度相关（p = 0.0079，总Prins每单位变化增加4.42 ml，95%置信区间[1.17 7.67]），但与低强度、细微WMH的变化无关，而后者决定了体积变化。
新指标实用且计算简单。它对图像处理方法和扫描协议的变化具有鲁棒性，并且对细微和严重的白质损伤敏感。
